CN112187499B - Device partition management and division method in device network - Google Patents
Device partition management and division method in device network Download PDFInfo
- Publication number
- CN112187499B CN112187499B CN201910595993.4A CN201910595993A CN112187499B CN 112187499 B CN112187499 B CN 112187499B CN 201910595993 A CN201910595993 A CN 201910595993A CN 112187499 B CN112187499 B CN 112187499B
- Authority
- CN
- China
- Prior art keywords
- network
- equipment
- nodes
- node
- equipment network
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/04—Network management architectures or arrangements
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
Abstract
The invention discloses a partitioning method for equipment network partition management, which improves the detection precision of a community structure by taking modularity Q as a standard, and can be applied to a non-right equipment network and a weighted equipment network. The method has the following overall framework: 1) constructing an equipment network model; 2) calculating the key degree of each node in the network on the influence of the network structure, and labeling the nodes in a descending manner; 3) nodes in the network are independently clustered, and Q is calculated; 4) traversing nodes according to the node numbers, and merging the local modularity which is the most target after each node and the neighbor nodes are labeled with the same number with the neighbor nodes with connecting edges; 5) calculating Q1If Q is1>Q, repeating 3) to 5), otherwise, outputting the network community structure corresponding to the Q.
Description
Technical Field
The invention provides a partitioning method for device partition management in a device network, and belongs to the technical field of complex network and system science.
Background
The community structure is an important research content in a complex network, and community structure analysis of the network is beneficial to more comprehensively and integrally knowing distribution and connection among the network interiors. Meanwhile, on the application level, the community structure of the network is analyzed, the network can be scientifically and effectively managed in a partitioned mode, and multiple constraint conditions can be combined on the basis of partitioning. For the device network, the partition management of the devices can realize the function cooperation maximization, and can discover the devices or device groups which have larger influence on the network structure in the device network, thereby effectively providing a basis for the maintenance and the extension of the subsequent devices and the structure optimization of the device network.
The air traffic control technology support system is a typical equipment network, and the network realizes an important functional infrastructure set for air traffic safety and smoothness, wherein the network comprises various functional equipment such as communication, navigation, monitoring and the like. At present, in the field of air traffic control, the community structure of the waypoint network is rarely researched, Gurtern and the like utilize the Louvain algorithm to research the European airspace structure and mainly analyze the network structures of the waypoint network and the sector network, and the analysis of the network structures has a certain guiding effect on the re-planning and management of the European airspace.
The community structure of a weighted air traffic control technology support system network is researched, and the community structure aims to identify a small area with weak technical support capability in the weighted air traffic control technology support network, particularly to a small area with large influence on communication among two or more large communities, wherein the small area is easy to generate large negative influence on safe and effective operation of the whole network when a major natural disaster comes, and even serious accidents such as network paralysis and the like. The invention mainly provides a partitioning method for equipment partition management in an equipment network, which can realize the identification of an area with weak technical support capability in a weighted air traffic control technical support system network and provide a theoretical basis for the expansion planning of the future network air traffic control technical support equipment.
Disclosure of Invention
The invention aims to provide a partitioning method for device partition management in a device network, which is an unsupervised aggregation heuristic algorithm and can realize partitioning of device partitions according to different function management requirements, and the method takes network modularity as an index and combines the importance degree of each device on the overall structure of the network, thereby scientifically improving the accuracy of network partitioning.
In order to achieve the purpose, the invention adopts the following technical scheme.
The method for partitioning and managing the device network comprises the following steps.
(1) And constructing a complex network model of the equipment network and a weight matrix thereof.
The network can be modeled as a complex network
Including a set of nodes of devices
A set of edges in a collaborative relationship between devices
Adjacent matrix
Weight matrix of multifunctional cooperative ability between devices
And weight matrix of geographical distance information between devices
。
Wherein
As a function of the cooperative relationship between devices i and j,
different categories of coordination may exist between devices i and j for the category parameter of coordination.
Wherein d isijRepresenting the actual geographical distance, D, between devices i and jmaxRepresenting the maximum geographical distance length in the network.
(2) The criticality of the impact of individual devices in a network of devices on the network architecture is ranked and numbered.
Judging the key degree of the influence of single equipment in the unauthorized equipment network on the network structure, and according to the equipment network node degree:
。
weighting criticality of individual device impact on network structure in device networkJudging, namely dividing the network into two networks, namely a device network with multiple cooperative relationship parameters and a device network only considering the geographical distance information between devices according to the weighting strength of the device network nodes;
the method is applied to a device network with multiple collaborative relationship parameters.
For device networks that only consider geographical distance information between devices.
Calculating the key degree of the influence of the single equipment in the equipment network on the network structure, and performing descending order according to the actual region division requirement and the key degree value of the influence of the single equipment in the equipment network on the network structure, thereby numbering the equipment nodes in the equipment network again.
(3) And regarding each node numbered in the network as an independent community, calculating the modularity Q, wherein the larger the Q value is, the better the division degree of the equipment network community is, and the more the division result meets the actual requirement.
Taking a weighted device network with multiple cooperation parameters as an example, a weight matrix of multi-functional cooperation capability between devices is used, and the modularity Q is expressed as.
Wherein
Representing the sum of all the edge weights in the network,
representing the difference between the internal and external weights of the node, the matrixC i×r A community belonging tag representing each node.
If the network of the unauthorized device needs to be considered, using an adjacency matrix A; this uses the geographical distance matrix L if only the network of devices based on geographical distance information needs to be considered.
(4) Traversing nodes in the network according to the node number label, aiming at each equipment node, trying to mark the node and the adjacent node connected with the node as the same community, calculating the modularity each time and marking as Qi ’Finding the maximum module degree of the local node division result, and recording as max Qi ’And continuing to calculate the next node until all the nodes are traversed to end to obtain the division result of the equipment network, calculating the modularity of the equipment network at the moment, and recording the modularity as Q1。
(5) Comparing Q with Q1If Q is greater than or equal to1>And Q, repeating the steps (3) to (5), otherwise, outputting the network node community division result corresponding to the Q value.
Compared with the prior art, the invention has the beneficial effects that.
The accuracy of the community detection method is further improved, the identification of small areas with weak technical support capability in the weighted air traffic control technical support network is realized, especially the identification of small areas with large influence on communication among two or more large communities is realized, and the expansion planning of the air traffic control technical support system network can be effectively guided, so that the resistance capability under the condition of serious disasters is enhanced.
Drawings
FIG. 1 is a block diagram of the overall concept of a community structure detection method according to an embodiment of the present invention.
Fig. 2 is a weighted network model of the air traffic control technical support system in the southwest area of china.
Fig. 3 shows the coverage of radar equipment and vhf equipment in the air traffic control technology support system in the southwest of china.
Fig. 4 is a comparison diagram of the community structure division result of the weighted air-hung technical support network in the southwest area of china by each method in the embodiment of the present invention, and the community structure division diagram of the Louvain algorithm and the division method of the present invention is sequentially shown from top to bottom.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
In the embodiment, a network of a navigation station in the southwest area of china is constructed from the perspective of a complex network theory, and the network is a typical device network with multiple coordination relationships. The network model is based on geographical positions built by various devices in an actual air traffic control technology support system, nodes in the network are navigation stations, a route between the two navigation stations is arranged at the same time, and the weight of the route is the support efficiency of the radar device, the very high frequency device and the navigation devices. For radar equipment and very high frequency equipment, the larger the service coverage area of an airway is, the higher the corresponding guarantee efficiency is. For navigation equipment, the guarantee capability of the navigation equipment on an air route is high, and the main influence factor is the geographical distance.
Fig. 2 shows a weighting network model of the air traffic control technology support system in the southwest area of china, which depicts the position distribution of network nodes and the weight difference of edges in the network, and the thicker the edge is, the larger the weight value is. Fig. 3 depicts the coverage of radar equipment and very high frequency equipment in the air traffic control technical support system in the southwest region of china.
Fig. 4 shows different community division methods for the weighted air traffic control technical support system network, and by comparison, the method realizes the detection of small and medium communities in the network. The small community shows that the small community has a great influence on the connectivity of two nearby large communities, once the equipment in the area is damaged and the air route is interrupted, the smooth and safe air traffic from the junior region to the guiyang region is seriously influenced, so that the equipment needs to be added in the area to improve the survivability of the network to serious disasters.
Claims (1)
1. The method for managing and dividing the device network partitions is characterized by comprising the following specific steps of:
(1) constructing a complex network model of the equipment network and a weight matrix thereof:
the network can be modeled into a complex network, wherein the complex network comprises a set of devices as nodes, a set of edges which are cooperative relations among the devices, an adjacency matrix, a weight matrix of multifunctional cooperative capacity among the devices and a weight matrix of geographic distance information among the devices;
(2) the key degree sequence and number of the influence of single equipment on the network structure in the equipment network are as follows:
judging the key degree of the influence of single equipment in the unauthorized equipment network on the network structure according to the equipment network node degree; the method comprises the following steps of judging the key degree of influence of single equipment on a network structure in a weighted equipment network, and dividing the judgment into two networks, namely an equipment network with multiple cooperative relation parameters and an equipment network only considering geographical distance information between the equipment according to the weighted strength of equipment network nodes;
calculating the key degree of the influence of the single equipment in the equipment network on the network structure, and performing descending order according to the actual area division requirement and the key degree value of the influence of the single equipment in the equipment network on the network structure, thereby numbering the equipment nodes in the equipment network again;
(3) each node numbered in the network is regarded as an independent community, the modularity Q is calculated, the larger the modularity value is, the better the division degree of the equipment network community is, and the more the division result meets the actual requirement; calculating a neighboring matrix used by the unweighted equipment network, calculating a weighted equipment network, for example, considering a weighted matrix of multifunctional cooperative capability among weighted equipment network using equipment with multiple cooperative parameters, for example, only considering an equipment network using a geographic distance matrix based on geographic distance information;
(4) traversing nodes in the network according to the node number labels, aiming at each equipment node, trying to mark the nodes and adjacent nodes connected with the nodes as the same community, calculating the modularity each time, finding the maximum modularity of the partial node division result, continuing to calculate the next node until all the nodes are traversed, obtaining the division result of the equipment network, calculating the modularity of the equipment network at the moment, and marking as Q1；
(5) Comparing Q with Q1If Q is greater than or equal to1>And Q, repeating the steps (3) to (5), otherwise, outputting the network node community division result corresponding to the Q value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910595993.4A CN112187499B (en) | 2019-07-03 | 2019-07-03 | Device partition management and division method in device network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910595993.4A CN112187499B (en) | 2019-07-03 | 2019-07-03 | Device partition management and division method in device network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112187499A CN112187499A (en) | 2021-01-05 |
| CN112187499B true CN112187499B (en) | 2021-12-03 |
Family
ID=73914472
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910595993.4A Active CN112187499B (en) | 2019-07-03 | 2019-07-03 | Device partition management and division method in device network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112187499B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103208027A (en) * | 2013-03-13 | 2013-07-17 | 北京工业大学 | Method for genetic algorithm with local modularity for community detecting |
| CN104680262A (en) * | 2015-03-18 | 2015-06-03 | 国网上海市电力公司 | Receiving-end grid optimal layering and districting scheme obtaining method |
| CN104750929A (en) * | 2015-03-30 | 2015-07-01 | 华南理工大学 | Rail transit service efficiency invulnerability measurement method combining network point right |
| CN109410577A (en) * | 2018-11-09 | 2019-03-01 | 东北林业大学 | Adaptive traffic control sub-area division method based on Spatial Data Mining |
| CN109921921A (en) * | 2019-01-26 | 2019-06-21 | 复旦大学 | The detection method and device of aging stability corporations in a kind of time-varying network |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| BR112015031754A2 (en) * | 2013-06-18 | 2017-07-25 | Cmi Uvk Gmbh | heating module for use in a pyrohydrolysis reactor |
-
2019
- 2019-07-03 CN CN201910595993.4A patent/CN112187499B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103208027A (en) * | 2013-03-13 | 2013-07-17 | 北京工业大学 | Method for genetic algorithm with local modularity for community detecting |
| CN104680262A (en) * | 2015-03-18 | 2015-06-03 | 国网上海市电力公司 | Receiving-end grid optimal layering and districting scheme obtaining method |
| CN104750929A (en) * | 2015-03-30 | 2015-07-01 | 华南理工大学 | Rail transit service efficiency invulnerability measurement method combining network point right |
| CN109410577A (en) * | 2018-11-09 | 2019-03-01 | 东北林业大学 | Adaptive traffic control sub-area division method based on Spatial Data Mining |
| CN109921921A (en) * | 2019-01-26 | 2019-06-21 | 复旦大学 | The detection method and device of aging stability corporations in a kind of time-varying network |
Non-Patent Citations (2)
| Title |
|---|
| 基于复杂网络视角的导航台设备网络鲁棒性分析;李朋，韩松臣;《计算机技术与发展》;20190318;第28卷(第12期);127-131 * |
| 考虑地理距离的复杂网络社区挖掘算法;陈娱;《地理信息科学学报》;20130630;第15卷(第3期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112187499A (en) | 2021-01-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108109382B (en) | Congestion point, congestion line and congestion area discovery method based on composite network | |
| CN107241512B (en) | Intercity Transportation trip mode judgment method and equipment based on data in mobile phone | |
| CN102521973B (en) | A kind of mobile phone switches the road matching method of location | |
| CN110213724B (en) | Pseudo base station motion trajectory identification method | |
| CN105608505A (en) | Cellular signaling data based track traffic travel mode identification method for resident | |
| CN107872808B (en) | WLAN station address prediction analysis method and device | |
| CN105512344B (en) | A kind of querying method of indoor moving object relative location | |
| CN106488405A (en) | A kind of position predicting method merging individuality and neighbour's movement law | |
| CN114037175B (en) | Large-scale public transportation network hierarchical optimization method based on multi-scale clustering | |
| CN107092984A (en) | A kind of network function end node propagation prediction method based on cascading failure | |
| CN108447255B (en) | Urban road dynamic traffic network structure information system | |
| CN110532508B (en) | Multi-objective optimization path selection method of power transmission line based on stepping ring grid | |
| CN104066058A (en) | Wireless local area network (WLAN) indoor positioning method based on overlapping of two sets of fingerprints | |
| CN110991913B (en) | Busy airport peak time congestion risk analysis method | |
| CN107845260A (en) | A kind of recognition methods of user's bus trip mode | |
| CN109121083B (en) | Indoor positioning method based on fingerprint similarity of AP (Access Point) sequence | |
| CN116796904A (en) | Method, system, electronic equipment and medium for predicting new line passenger flow of rail transit | |
| Li et al. | Estimating crowd flow and crowd density from cellular data for mass rapid transit | |
| CN112187499B (en) | Device partition management and division method in device network | |
| CN110246372A (en) | The Expansion Planning method of aviation support equipment | |
| CN106408936A (en) | Mobile-phone-data-based real-time detection method for abnormal event of highway | |
| CN110135451A (en) | A kind of track clustering method arriving line-segment sets distance based on point | |
| CN109743723A (en) | A method of cellular base station data are assigned to peripheral space unit | |
| CN111008730B (en) | Crowd concentration prediction model construction method and device based on urban space structure | |
| CN111125925A (en) | Terminal area airspace space-time correlation analysis method driven by aircraft track data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |